U.S. patent application number 14/776735 was filed with the patent office on 2016-02-04 for characteristic-value calculating device, characteristic-value calculating method, and recording medium.
This patent application is currently assigned to Oki Electric Industry Co., Ltd.. The applicant listed for this patent is OKI ELECTRIC INDUSTRY CO., LTD.. Invention is credited to Kurato MAENO, Michiyo MATSUI, Daisuke OKUYA.
Application Number | 20160030006 14/776735 |
Document ID | / |
Family ID | 51791300 |
Filed Date | 2016-02-04 |
United States Patent
Application |
20160030006 |
Kind Code |
A1 |
OKUYA; Daisuke ; et
al. |
February 4, 2016 |
CHARACTERISTIC-VALUE CALCULATING DEVICE, CHARACTERISTIC-VALUE
CALCULATING METHOD, AND RECORDING MEDIUM
Abstract
[Object] To provide a characteristic-value calculating device, a
characteristic-value calculating method, and a recording medium
that can extract a characteristic value for more accurately
recognizing the state of a moving object, such as a human, from a
Doppler signal. [Solution] A characteristic-value calculating
device includes an acquiring unit that acquires a Doppler signal,
an extracting unit that extracts a time-series signal constituted
of a predetermined frequency component from the Doppler signal
acquired by the acquiring unit, a selecting unit that selects a
signal value at a predetermined interval from the time-series
signal extracted by the extracting unit, and a calculating unit
that calculates higher-order local autocorrelation based on the
signal value selected by the selecting unit.
Inventors: |
OKUYA; Daisuke; (Tokyo,
JP) ; MAENO; Kurato; (Tokyo, JP) ; MATSUI;
Michiyo; (Tokyo, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
OKI ELECTRIC INDUSTRY CO., LTD. |
Tokyo |
|
JP |
|
|
Assignee: |
Oki Electric Industry Co.,
Ltd.
Tokyo
JP
|
Family ID: |
51791300 |
Appl. No.: |
14/776735 |
Filed: |
October 1, 2013 |
PCT Filed: |
October 1, 2013 |
PCT NO: |
PCT/JP2013/076641 |
371 Date: |
September 14, 2015 |
Current U.S.
Class: |
702/150 |
Current CPC
Class: |
G01S 7/4802 20130101;
G01S 17/58 20130101; G01S 7/415 20130101; G08B 13/16 20130101; G01S
15/58 20130101; A61B 8/488 20130101; G01S 7/539 20130101; G01S
13/56 20130101; A61B 8/5223 20130101; G01S 17/88 20130101; G01S
15/88 20130101; G01S 15/523 20130101 |
International
Class: |
A61B 8/08 20060101
A61B008/08; G01S 15/88 20060101 G01S015/88; G01S 17/88 20060101
G01S017/88 |
Foreign Application Data
Date |
Code |
Application Number |
Apr 26, 2013 |
JP |
2013-093529 |
Claims
1. A characteristic-value calculating device comprising: an
acquiring unit that acquires a Doppler signal; an extracting unit
that extracts a time-series signal constituted of a predetermined
frequency component from the Doppler signal acquired by the
acquiring unit; a selecting unit that selects a signal value at a
predetermined interval from the time-series signal extracted by the
extracting unit; and a calculating unit that calculates
higher-order local autocorrelation based on the signal value
selected by the selecting unit.
2. The characteristic-value calculating device according to claim
1, further comprising: a vector normalization unit that performs
vector normalization on the signal value selected by the selecting
unit, wherein the calculating unit calculates the higher-order
local autocorrelation based on the signal value normalized by the
vector normalization unit.
3. The characteristic-value calculating device according to claim
1, further comprising: a dimensional compression unit that performs
dimensional compression on the higher-order local autocorrelation
calculated by the calculating unit.
4. The characteristic-value calculating device according to claim
1, further comprising: a preprocessing unit that performs
predetermined signal processing on the Doppler signal acquired by
the acquiring unit, wherein the extracting unit extracts the
time-series signal from the Doppler signal signal-processed by the
preprocessing unit.
5. The characteristic-value calculating device according to claim
4, wherein the preprocessing unit performs offset adjustment on the
Doppler signal acquired by the acquiring unit.
6. The characteristic-value calculating device according to claim
1, wherein the extracting unit extracts the time-series signal
constituted of a frequency component arising from human movement
from the Doppler signal acquired by the acquiring unit.
7. The characteristic-value calculating device according to claim
1, further comprising: a recognizing unit that recognizes a state
of a space subjected to observation of the Doppler signal acquired
by the acquiring unit based on the higher-order local
autocorrelation calculated by the calculating unit.
8. The characteristic-value calculating device according to claim
7, wherein the recognizing unit recognizes at least one of
unmanned, rest, and activity states as the state of the space.
9. The characteristic-value calculating device according to claim
1, further comprising: an observing unit that observes the Doppler
signal and outputs the Doppler signal to the acquiring unit.
10. A characteristic-value calculating method comprising: a step
for acquiring a Doppler signal; a step for extracting a time-series
signal constituted of a predetermined frequency component from the
acquired Doppler signal; a step for selecting a signal value at a
predetermined interval from the extracted time-series signal; and a
step for calculating higher-order local autocorrelation based on
the selected signal value.
11. A recording medium having a program stored therein, the program
causing a computer to execute: a step for acquiring a Doppler
signal; a step for extracting a time-series signal constituted of a
predetermined frequency component from the acquired Doppler signal;
a step for selecting a signal value at a predetermined interval
from the extracted time-series signal; and a step for calculating
higher-order local autocorrelation based on the selected signal
value.
Description
TECHNICAL FIELD
[0001] The present invention relates to a characteristic-value
calculating device, a characteristic-value calculating method, and
a recording medium.
BACKGROUND ART
[0002] In recent years, there has been developed a technology in
which a sensor is installed in a specific area, such as inside a
building, for detecting, for example, the state of a person present
at that location.
[0003] For example, Patent Literatures 1 and 2 below disclose
technologies for detecting the presence of a person inside a
building based on body movement or biological information obtained
from a sensor. More specifically, Patent Literature 1 discloses a
technology for estimating the state in the building from three
states, namely, unmanned, rest, and activity states, by performing
a threshold determination process based on the intensity of a
signal of a Doppler sensor radiated into the room and a variance
value. Patent Literature 2 discloses a technology for extracting a
respiration component, a heartbeat component, and a body-movement
component by performing frequency conversion and a filtering
process on a signal from a pressure sensor attached to a life
support device so as to detect the absence or presence from these
components or to detect an emergency situation.
[0004] Furthermore, Patent Literature 3 below discloses a
technology for differentiating sleeping states and performing
problem detection intended for performing monitoring during
sleeping. More specifically, Patent Literature 3 discloses a
technology for detecting, for example, respiration, a roll-over, or
a fall-off from a bed by radiating a signal from a Doppler sensor
toward a person sleeping on the bed and then performing a threshold
determination process based on information, such as an operating
time, speed, and direction, obtained from the Doppler signal.
CITATION LIST
Patent Literature
[0005] Patent Literature 1: JP 2011-215031A
[0006] Patent Literature 2: JP 2004-174168A
[0007] Patent Literature 3: JP 2012-5745A
SUMMARY OF INVENTION
Technical Problem
[0008] However, the technologies disclosed in Patent Literatures 1
to 3 described above are problematic in that, since only a part of
the human biological information, such as the signal intensity, the
variance, the frequency component, and the speed relative to the
sensor, is used, false detection may occur if there is disturbance
that resembles these pieces of information within the area.
[0009] For example, in the technology disclosed in Patent
Literature 1 described above, if there is a small animal, such as a
pet or an insect, it is difficult to distinguish the small animal
from a human solely based on the signal intensity and the variance
thereof. In the technology disclosed in Patent Literature 2
described above, if there is an electrical household device that
operates in a cycle similar to the human respiration cycle, it is
not possible to detect accurate biological information.
Furthermore, with regard to the technology disclosed in Patent
Literature 3, since the technology is specialized for monitoring a
person on a bed, human respiration or body movement can be
extracted with respect to the limited space on the bed. However, in
view of applying the technology to a wide range, such as the entire
interior of the building, the technology is problematic in that
differentiation from disturbance is not taken into
consideration.
[0010] In view of the problems mentioned above, an object of the
present invention is to provide a new and improved
characteristic-value calculating device, characteristic-value
calculating method, and recording medium that can extract, from a
Doppler signal, a characteristic value for more accurately
recognizing the state of a moving object, such as a human.
Solution to Problem
[0011] To solve the problem, according to an aspect of the present
invention, there is provided a characteristic-value calculating
device including: an acquiring unit that acquires a Doppler signal;
an extracting unit that extracts a time-series signal constituted
of a predetermined frequency component from the Doppler signal
acquired by the acquiring unit; a selecting unit that selects a
signal value at a predetermined interval from the time-series
signal extracted by the extracting unit; and a calculating unit
that calculates higher-order local autocorrelation based on the
signal value selected by the selecting unit.
[0012] The characteristic-value calculating device may further
include: a vector normalization unit that performs vector
normalization on the signal value selected by the selecting unit.
The calculating unit may calculate the higher-order local
autocorrelation based on the signal value normalized by the vector
normalization unit.
[0013] The characteristic-value calculating device may further
include: a dimensional compression unit that performs dimensional
compression on the higher-order local autocorrelation calculated by
the calculating unit.
[0014] The characteristic-value calculating device may further
include: a preprocessing unit that performs predetermined signal
processing on the Doppler signal acquired by the acquiring unit.
The extracting unit may extract the time-series signal from the
Doppler signal signal-processed by the preprocessing unit.
[0015] The preprocessing unit may perform offset adjustment on the
Doppler signal acquired by the acquiring unit.
[0016] The extracting unit may extract the time-series signal
constituted of a frequency component arising from human movement
from the Doppler signal acquired by the acquiring unit.
[0017] The characteristic-value calculating device may further
include: a recognizing unit that recognizes a state of a space
subjected to observation of the Doppler signal acquired by the
acquiring unit based on the higher-order local autocorrelation
calculated by the calculating unit.
[0018] The recognizing unit may recognize at least one of unmanned,
rest, and activity states as the state of the space.
[0019] The characteristic-value calculating device may further
include: an observing unit that observes the Doppler signal and
outputs the Doppler signal to the acquiring unit.
[0020] To solve the problem, according to another aspect of the
present invention, there is provided a characteristic-value
calculating method including: a step for acquiring a Doppler
signal; a step for extracting a time-series signal constituted of a
predetermined frequency component from the acquired Doppler signal;
a step for selecting a signal value at a predetermined interval
from the extracted time-series signal; and a step for calculating
higher-order local autocorrelation based on the selected signal
value.
[0021] To solve the problem, according to another aspect of the
present invention, there is provided a recording medium having a
program stored therein, the program causing a computer to execute:
a step for acquiring a Doppler signal; a step for extracting a
time-series signal constituted of a predetermined frequency
component from the acquired Doppler signal; a step for selecting a
signal value at a predetermined interval from the extracted
time-series signal; and a step for calculating higher-order local
autocorrelation based on the selected signal value.
Advantageous Effects of Invention
[0022] As described above, according to the present invention, a
characteristic value for more accurately recognizing the state of a
moving object, such as a human, can be extracted from a Doppler
signal.
BRIEF DESCRIPTION OF DRAWINGS
[0023] FIG. 1 is a diagram schematically illustrating a state
recognizing system according to an embodiment of the present
invention.
[0024] FIG. 2 is a block diagram illustrating the configuration of
the state recognizing system according to an embodiment of the
present invention.
[0025] FIG. 3 is a flowchart illustrating an operation of the state
recognizing system according to an embodiment of the present
invention.
[0026] FIG. 4 is a diagram illustrating frequency distribution of
average frequencies by FFT in a one-second interval.
[0027] FIG. 5 is a diagram illustrating frequency distribution of
average frequencies by FFT in a ten-second interval.
[0028] FIG. 6 is a diagram illustrating frequency distribution of a
first principal component of higher-order local autocorrelation in
a one-second interval in accordance with an embodiment of the
present invention.
DESCRIPTION OF EMBODIMENTS
[0029] A preferred embodiment of the present invention will be
described in detail below with reference to the appended drawings.
It should be noted that, in the present description and the
drawings, components having substantially identical functions and
configurations will be given the same reference signs, and
redundant descriptions thereof will be omitted.
1. Overview of State Recognizing System According to Embodiment of
Present Invention
[0030] First, an overview of a state recognizing system according
to an embodiment of the present invention will be described with
reference to FIG. 1.
[0031] FIG. 1 is a diagram schematically illustrating the state
recognizing system according to the embodiment of the present
invention. As shown in FIG. 1, the state recognizing system
according to the embodiment of the present invention has a Doppler
sensor 1,a characteristic-value calculating device 2, and a
recognizing device 3. As shown in FIG. 1, the Doppler sensor 1 is
installed at, for example, one corner of a room, transmits a
light-based, electromagnetic-based, or acoustic-based transmission
wave toward the interior of the room, which is a detection area,
and receives a reflection wave (reception wave) reflected by a
moving object (reflective object), such as a person or an animal,
in the room. Although FIG. 1 shows an example in which one person
is present in the room, there may be a plurality of people or may
be an animal other than humans.
[0032] The Doppler sensor 1 generates a Doppler signal based on the
transmission wave and the reception wave and outputs the Doppler
signal to the characteristic-value calculating device 2. Then, the
characteristic-value calculating device 2 extracts, from the
Doppler signal, a characteristic value for more accurately
recognizing the state of the moving object in the room and outputs
the characteristic value to the recognizing device 3.
[0033] The characteristic-value calculating device 2 according to
the embodiment of the present invention extracts higher-order local
autocorrelation (HLAC) as such a characteristic value. Higher-order
local autocorrelation is a characteristic value that expresses a
correlation quantity of a single signal or a plurality of signals
in local time. In the embodiment of the present invention,
higher-order local autocorrelation is applied to a Doppler signal
so that a change in phase difference and the periodicity of
movement (human body movement or biological signal) within a target
area can be extracted.
[0034] Based on the characteristic value extracted by the
characteristic-value calculating device 2, the recognizing device 3
recognizes whether the room is in an unmanned state or a manned
state, and if the room is in a manned state, the recognizing device
3 recognizes whether the room is in an activity state or a rest
state. The term "unmanned" refers to a state where there is no one
present within the target area. The term "rest" refers to a state
where there is a person present within the target area but only
breathing without actively moving (e.g., a state where the person
is sitting in a chair or on the floor, is standing, or is lying
down). The term "activity" refers to a state where there is a
person present within the target area and actively moving, such as
moving hands and feet (e.g., positionally moving or stamping
feet).
[0035] The overview of the state recognizing system has been
described above. The state recognizing system according to the
embodiment of the present invention will be described in detail
below with reference to FIG. 2 to FIG. 6.
2. Embodiment of Present Invention
[2.1. Configuration]
[0036] FIG. 2 is a block diagram illustrating the configuration of
the state recognizing system according to the embodiment of the
present invention. As shown in FIG. 2, the state recognizing system
has the Doppler sensor 1,the characteristic-value calculating
device 2, and the recognizing device 3.
(Doppler Sensor 1)
[0037] The Doppler sensor 1 has a function of an observing unit
that observes a Doppler signal indicating movement of a moving
object within a target observation space (detection area). The
Doppler sensor 1 has a configuration for emitting an output signal
from a local oscillator via a transmission antenna and receiving a
reflection wave from a target object via a reception antenna. When
the Doppler sensor 1 receives a reflection wave from a target
object via the reception antenna, the Doppler sensor 1 divides the
reception signal into two signals by using a distributor and delays
one of the signals by 90 degrees by using a phase shifter. Since
the reflection wave from the moving object is frequency-modulated
due to a Doppler effect, a phase difference occurs in the
signals.
[0038] In this embodiment, the two waves with phases different from
each other by 90 degrees, which are obtained by the Doppler sensor
1,will be defined as V.sub.I(t) and V.sub.Q(t) as shown in Equation
1 below. The subscripts I and Q denote in-phase and quadrature,
respectively.
[ Math . 1 ] V I ( t ) = A I sin ( 4 .pi. R ( t ) .lamda. + .phi. 0
) + O I + w I V Q ( t ) = A Q sin ( 4 .pi. R ( t ) .lamda. + .phi.
0 + .pi. 2 ) + O Q + w Q ( Equation 1 ) ##EQU00001##
[0039] In Equation 1, A denotes the amplitude of each signal,
.lamda., denotes the wavelength, R(t) denotes the distance between
the Doppler sensor 1 and the target object at a time point t,
.phi..sub.O denotes the initial phase, O denotes direct-current
offset, and w denotes a noise component. A method of how Equation 1
is derived is disclosed in "Droitcour, A. D. et al. "Range
correlation and I/Q performance benefits in single-chip silicon
Doppler radars for noncontact cardiopulmonary monitoring" Microwave
Theory and Techniques, IEEE Transactions, Vol. 52, No. 3, pp.
838-848, March 2004''.
[0040] The Doppler sensor 1 outputs the signals V.sub.I(t) and
V.sub.Q(t) to the characteristic-value calculating device 2. The
signals V.sub.I(t) and V.sub.Q(t) may also be referred to as
Doppler signals hereinafter.
(Characteristic-Value Calculating Device 2)
[0041] The characteristic-value calculating device 2 extracts a
characteristic value indicating the state of the target object
within the detection area from the Doppler signals output from the
Doppler sensor 1. The characteristic-value calculating device 2
functions as a preprocessing unit 21, a data storage unit 22, a
filtering unit 23, a subsampling unit 24, a vector normalization
unit 25, a higher-order local autocorrelation calculating unit 26,
a dimensional compression unit 27, and a result output unit 28.
Preprocessing Unit 21
[0042] The preprocessing unit 21 has a function of an acquiring
unit that acquires the Doppler signals from the Doppler sensor 1
and a function for performing predetermined signal processing
(preprocessing) on the Doppler signals. The preprocessing executed
by the preprocessing unit 21 may include, for example, conversion
to digital signals by sampling the signal intensity, offset
adjustment of the signals, and removal of direct-current components
by applying a high-pass filter. In accordance with the offset
adjustment performed by the preprocessing unit 21, the
characteristic-value calculating device 2 can also deal with a case
where different types of Doppler sensors 1 are connected.
[0043] The Doppler sensor 1 and the preprocessing unit 21 are
realized as separate hardware units in the configuration shown in
FIG. 2 but may also be realized as a single hardware unit.
Specifically, the preprocessing unit 21 may be included in the
HMD1, or the characteristic-value calculating device 2 may be
included in the Doppler sensor 1. Furthermore, the preprocessing
unit 21 may perform subsampling on the acquired Doppler signals so
as to remove noise components superposed due to supply voltage as
well as redundant high-frequency regions, thereby reducing the
throughput in subsequent blocks.
[0044] The preprocessing unit 21 outputs the preprocessed Doppler
signals to the data storage unit 22.
Data Storage Unit 22
[0045] The data storage unit 22 has a function of storing the
Doppler signals output from the preprocessing unit 21. The data
storage unit 22 is realized by, for example, a hard disc drive
(ADD), a solid-state memory such as a flash memory, a memory card
containing a solid-state memory, an optical disk, a magneto-optical
disk, or a hologram memory.
[0046] The data storage unit 22 outputs the stored Doppler signals
to the filtering unit 23.
[0047] In the present invention, operation may be performed in
real-time or in non-real-time. Therefore, the data storage unit 22
may output the Doppler signals output from the preprocessing unit
21 to the filtering unit 23 in real-time, or may output the stored
Doppler signals in non-real-time. Furthermore, the preprocessing
unit 21 and the data storage unit 22 may be separated from the
characteristic-value calculating device 2 and be integrated with
the Doppler sensor 1. In this case, the data storage unit 22 stores
a signal observed by the Doppler sensor 1 and preprocessed by the
preprocessing unit 21, and the subsequent processing is performed
by, for example, a separate general-purpose personal computer, so
that the device that performs sensing can be reduced in size.
Filtering Unit 23
[0048] The filtering unit 23 has a function of an extracting unit
that extracts a time-series signal constituted of a predetermined
frequency component from the Doppler signals preprocessed by the
preprocessing unit 21. Specifically, the filtering unit 23 performs
frequency-filtering on the Doppler signals output from the data
storage unit 22.
[0049] Although the sampling rate of a Doppler sensor is normally
several kHz, a frequency component of human movement is about 0.1
Hz to several tens of Hz. Therefore, the filtering unit 23 sets a
cutoff frequency in accordance with a frequency band to which
noteworthy components, such as respiration, heartbeat, and body
movement, may belong, and applies a band-pass filter or a low-pass
filter so as to extract only a signal component deriving from human
movement.
[0050] The filtering method in the filtering unit 23 is not limited
to a specific method. The method used in the filtering unit 23 may
be selected from among a method that employs conversion to a
frequency domain based on Fourier transform, an infinite impulse
response (AIR) filter, and a finite impulse response (FIR) filter,
so long as a digital signal can be filtered.
[0051] The filtering unit 23 outputs the extracted signal to the
subsampling unit 24.
Subsampling Unit 24
[0052] The subsampling unit 24 has a function of a selecting unit
that selects a signal value at predetermined intervals from the
signal extracted by the filtering unit 23. More specifically, the
subsampling unit 24 selects (sub samples) a signal to be used by
the higher-order local autocorrelation calculating unit 26, to be
described later, from a signal sequence output from the filtering
unit 23.
[0053] Higher-order local autocorrelation to be calculated by the
higher-order local autocorrelation calculating unit 26, to be
described later, is defined in Equation 2 below.
[ Math . 2 ] r f ( .tau. 1 , , .tau. N ) = t f ( t ) f ( t + .tau.
1 ) f ( t + .tau. N ) ( Equation 2 ) ##EQU00002##
[0054] In above Equation 2, .tau. denotes a correlation width. The
subsampling unit 24 selects a plurality of signals to be used when
calculating the higher-order local autocorrelation shown in above
Equation 2 at intervals of the correlation width .tau.. In this
embodiment, the subsampling unit 24 selects six points, namely,
V.sub.I(t), V.sub.It+.tau.), V.sub.I(t+2.tau.), V.sub.Q(t),
V.sub.Q(t+.tau.), and V.sub.Q(t+2.tau.), which are to be used for
calculating second-order higher-order local autocorrelation of the
signals V.sub.I(t) and V.sub.Q(t). As a selection method, for
example, in the case of the signal V.sub.I(t+.tau.), the
subsampling unit 24 may use a point preceding V.sub.I(t) by the
correlation width .tau., or may use a smoothed signal by taking an
average of points before and after the signal by .tau./2.
[0055] The subsampling unit 24 outputs the signals of the six
selected points to the vector normalization unit 25.
Vector Normalization Unit 25
[0056] The vector normalization unit 25 has a function of
performing vector normalization on the signals selected by the
subsampling unit 24. Specifically, the vector normalization unit 25
performs vector normalization defined in Equation 3 below and
unifies the magnitude of the vectors at the six points to 1.
[ Math . 3 ] V I ' ( t ) = V I ( t ) L , V I ' ( t + .tau. ) = V I
( t + .tau. ) L , V I ' ( t + 2 .tau. ) = V I ( t + 2 .tau. ) L , V
Q ' ( t ) = V Q ( t ) L , V Q ' ( t + .tau. ) = V Q ( t + .tau. ) L
, V Q ' ( t + 2 .tau. ) = V Q ( t + 2 .tau. ) L where L = V I ( t )
2 + V I ( t + .tau. ) 2 + V I ( t + 2 .tau. ) 2 + V Q ( t ) 2 + V Q
( t + .tau. ) 2 + V Q ( t + 2 .tau. ) 2 ( Equation 3 )
##EQU00003##
[0057] In accordance with the vector normalization shown in above
Equation 3, the vector normalization unit 25 can alleviate a
difference in signal intensity based on a difference in distance
between the Doppler sensor 1 and a person subjected to
detection.
[0058] The vector normalization unit 25 outputs the signals
V.sub.I'(t), V.sub.I'(t+.tau.), V.sub.I'(t+2.tau.), V.sub.Q'(t),
V.sub.Q'(t+.tau.), and V.sub.Q'(t+2.tau.) of the six
vector-normalized points to the higher-order local autocorrelation
calculating unit 26.
Higher-Order Local Autocorrelation Calculating Unit 26
[0059] The higher-order local autocorrelation calculating unit 26
has a function of a calculating unit that calculates higher-order
local autocorrelation based on the signals output from the vector
normalization unit 25. More specifically, based on the signals of
the six points output from the vector normalization unit 25, the
higher-order local autocorrelation calculating unit 26 calculates a
total of 49 patterns of higher-order local autocorrelation defined
in Equation 4 below.
[Math. 4]
[0060] H.sub.1(t)=V'.sub.I(t), H.sub.2(t)=V'.sub.I(t)V'.sub.I(t),
H.sub.3(t)=V'.sub.I(t)V'.sub.I(t+.tau.),
H.sub.4(t)=V'.sub.I(t)V'.sub.I(t+2.tau.),
H.sub.5(t)=V'.sub.I(t)V'.sub.I(t)V'.sub.I(t),
H.sub.6(t)=V'.sub.I(t)V'.sub.I(t)V'.sub.I(t+.tau.),
H.sub.7(t)=V'.sub.I(t)V'.sub.I(t)V'.sub.I(t+2.tau.),
H.sub.8(t)=V'.sub.I(t)V'.sub.I(t+.tau.)V'.sub.I(t+.tau.),
H.sub.9(t)=V'.sub.I(t)V'.sub.I(t+2.tau.)V'.sub.I(t+2.tau.),
H.sub.10(t)=V'.sub.I(t)V'.sub.I(t+.tau.)V+.sub.I(t+2.tau.),
H.sub.11(t)=V'.sub.Q(t), H.sub.12(t)=V'.sub.Q(t)V'.sub.Q(t),
H.sub.13(t)=V'.sub.Q(t)V'.sub.Q(t+.tau.),
H.sub.14(t)=V'.sub.Q(t)V'.sub.Q(t+2.tau.),
H.sub.15(t)=V'.sub.Q(t)V'.sub.Q(t)V'.sub.Q(t),
H.sub.16(t)=V'.sub.Q(t)V'.sub.Q(t)V'.sub.Q(t+.tau.),
H.sub.17(t)=V'.sub.Q(t)V'.sub.Q(t)V'.sub.Q(t+2.tau.),
H.sub.18(t)=V'.sub.Q(t)V'.sub.Q(t+.tau.)V'.sub.Q(t+.tau.),
H.sub.19(t)=V'.sub.Q(t)V'.sub.Q(t+2.tau.)V'.sub.Q(t+2.tau.),
H.sub.20(t)=V'.sub.Q(t)V'.sub.Q(t+.tau.)V'.sub.Q(t+2.tau.),
H.sub.21(t)=V'.sub.I(t)V'.sub.Q(t),
H.sub.22(t)=V'.sub.I(t)V'.sub.Q(t+.tau.),
H.sub.23(t)=V'.sub.I(t)V'.sub.Q(t+2.tau.),
H.sub.24(t)=V'.sub.I(t+.tau.)V'.sub.Q(t),
H.sub.25(t)=V'.sub.I(t+.tau.)V'.sub.Q(t),
H.sub.26(t)=V'.sub.I(t)V'.sub.Q(t),
H.sub.27(t)=V'.sub.I(t)V'.sub.I(t)V'.sub.Q(t+.tau.),
H.sub.28(t)=V'.sub.I(t)V'.sub.Q(t+2.tau.),
H.sub.29(t)=V'.sub.I(t+.tau.)V'.sub.I(t+.tau.)V'.sub.Q(t), H.sub.30
(t)=V'.sub.I(t+2.tau.)V'.sub.I(t+2.tau.)V'.sub.Q(t),
H.sub.31(t)=V'.sub.I(t)V'.sub.I(t+.tau.)V'.sub.Q(t),
H.sub.32(t)=V'.sub.I(t)V'.sub.I(t+2.tau.)V'.sub.Q(t),
H.sub.33(t)=V'.sub.I(t)V'.sub.I(t+.tau.)V'.sub.Q(t+.tau.),
H.sub.34(t)=V'.sub.I(t)V'.sub.I(t+.tau.)V'.sub.Q(t+2.tau.),
H.sub.35(t)=V'.sub.I(t)V'.sub.I(t+2.tau.)V'.sub.Q(t+.tau.),
H.sub.36(t)=V'.sub.I(t)V'.sub.I(t+2.tau.)V'.sub.Q(t+2.tau.),
H.sub.37(t)=V'.sub.I(t+.tau.)V'.sub.I(t+2.tau.)V'.sub.Q(t),
H.sub.38(t)=V'.sub.Q(t)V'.sub.Q(t)V'.sub.I(t),
H.sub.39(t)=V'.sub.Q(t)V'.sub.Q(t)V'.sub.I(t+.tau.),
H.sub.40(t)=V'.sub.Q(t)V'.sub.Q(t)V'.sub.I(t+2.tau.),
H.sub.41(t)=V'.sub.Q(t+.tau.)V'.sub.Q(t+.tau.)V'.sub.I(t),
H.sub.42(t)=V'.sub.Q(t+2.tau.)V'.sub.Q(t+2.tau.)V.sub.I(t),
H.sub.43(t)=V'.sub.Q(t)V'.sub.Q(t+.tau.)V'.sub.I(t),
H.sub.44(t)=V'.sub.Q(t)V'.sub.Q(t+2.tau.)V'.sub.I(t),
H.sub.45(t)=V'.sub.Q(t)V'.sub.Q(t+.tau.)V'.sub.I(t+.tau.),
H.sub.46(t)=V'.sub.Q(t)V'.sub.Q(t+.tau.)V'.sub.I(t+2.tau.),
H.sub.47(t)=V'.sub.Q(t)V'.sub.Q(t+2.tau.)V'.sub.I(t+.tau.),
H.sub.48(t)=V'.sub.Q(t)V'.sub.Q(t+2.tau.)V'.sub.I(t+2.tau.),
H.sub.49(t)=V'.sub.Q(t+.tau.)V'.sub.Q(t+2.tau.)V'.sub.I(t),
[0061] The higher-order local autocorrelation calculating unit 26
outputs the calculated 49 patterns of higher-order local
autocorrelation to the dimensional compression unit 27.
Dimensional Compression Unit 27
[0062] The dimensional compression unit 27 has a function of
performing dimensional compression on the higher-order local
autocorrelation output from the higher-order local autocorrelation
calculating unit 26. For example, the dimensional compression unit
27 performs dimensional compression by using a dimensional
compression method such as a principal component analysis.
Therefore, the dimensional compression unit 27 can specify an
efficient characteristic value for state recognition by the
recognizing device 3, to be described later.
[0063] The dimensional compression unit 27 outputs the dimensional
compression result to the result output unit 28.
Result Output Unit 28
[0064] The result output unit 28 outputs the result output from the
dimensional compression unit 27 to the recognizing device 3 as a
characteristic value indicating the state within the detection
area. Alternatively, the result output unit 28 may directly output
the 49 patterns of higher-order local autocorrelation calculated by
the higher-order local autocorrelation calculating unit 26 as a
characteristic value indicating the state within the detection
area.
(Recognizing Device 3)
[0065] The recognizing device 3 has a function of a recognizing
unit that recognizes the state of the detection area of the Doppler
sensor 1 based on the characteristic value (higher-order local
autocorrelation) output from the result output unit 28. The
recognizing device 3 recognizes at least one of unmanned, rest, and
activity states as the state of the space.
[0066] Specifically, the recognizing device 3 first stores the
characteristic value output from the result output unit 28 while
clustering the characteristic value together with annotations such
as the unmanned, rest, and activity states. Then, the recognizing
device 3 calculates the degree of similarity between the
characteristic value output from the result output unit 28 and an
aggregate of clustered state vectors and recognizes similar
annotations as the state of the detection area. For calculating the
degree of similarity, the recognizing device 3 may use, for
example, a recognizer that is based on a support vector machine or
a hidden Marks model.
[0067] The recognizing device 3 may give the rest and activity
states a generic name of "manned" state so as to simply recognize
one of two states, that is, an unmanned state and a manned state.
Furthermore, the recognizing device 3 may be integrated with the
characteristic-value calculating device 2.
[0068] The configuration of the state system according to this
embodiment has been described above. Next, an operation of the
state system according to this embodiment will be described with
reference to FIG. 3.
[2.2. Operation]
[0069] FIG. 3 is a flowchart illustrating the operation of the
state recognizing system according to the embodiment of the present
invention. As shown in FIG. 3, in step S104, the Doppler sensor 1
first performs sensing in the detection area. Specifically, the
Doppler sensor 1 emits an output signal from the local oscillator
via the transmission antenna, receives a reflection wave from a
target object via the reception antenna, and obtains two waves of
Doppler signals V.sub.I(t) and V.sub.Q(t) shown in Equation 1
mentioned above based on the reflection wave and the reception
wave.
[0070] Then, in step S108, the preprocessing unit 21 performs
preprocessing on the two waves of Doppler signals V.sub.I(t) and
V.sub.Q(t) output from the Doppler sensor 1. The preprocessing
executed by the preprocessing unit 21 includes, for example,
conversion to digital signals by sampling the signal intensity,
offset adjustment of the signals, and removal of direct-current
components by applying a high-pass filter.
[0071] The Doppler signals preprocessed by the preprocessing unit
21 are stored by the data storage unit 22. The data storage unit 22
may output the Doppler signals output from the preprocessing unit
21 to the filtering unit 23 in real-time, or may output the stored
Doppler signals in non-real-time.
[0072] Subsequently, in step S112, the filtering unit 23 performs
filtering for extracting a frequency component deriving from human
movement from the Doppler signals output from the data storage unit
22. For example, as a frequency band to which noteworthy
components, such as respiration, heartbeat, and body movement, may
belong, the filtering unit 23 applies a band-pass filter or a
low-pass filter so as to extract only a frequency component ranging
between 0.1 Hz and several tens of Hz.
[0073] Then, in step S116, the subsampling unit 24 performs
subsampling from a signal sequence filtered by the filtering unit
23. Specifically, the subsampling unit 24 selects six points,
namely, V.sub.I(t), V.sub.I(t+.tau.), V.sub.I(t+2.tau.),
V.sub.Q(t), V.sub.Q(t+.tau.), and V.sub.Q(t+2.tau.), shown in
Equation 3 mentioned above.
[0074] Subsequently, in step S 120, the vector normalization unit
25 performs vector normalization on the signals output from the
subsampling unit 24. Specifically, the vector normalization unit 25
performs vector normalization shown in Equation 3 mentioned above
on the signals of the six points so as to alleviate a difference in
signal intensity based on a difference in distance between the
Doppler sensor 1 and a person subjected to detection.
[0075] Then, in step S114, the higher-order local autocorrelation
calculating unit 26 calculates higher-order local autocorrelation
based on the signals output from the vector normalization unit 25.
Specifically, based on the signal values of the six points, the
higher-order local autocorrelation calculating unit 26 calculates
49 patterns of higher-order local autocorrelation shown in Equation
4 mentioned above.
[0076] Subsequently, in step S108, the dimensional compression unit
27 performs dimensional compression on the higher-order local
autocorrelation output from the higher-order local autocorrelation
calculating unit 26. Specifically, the dimensional compression unit
27 specifies an efficient characteristic value for state
recognition from the 49 patterns of higher-order local
autocorrelation output from the higher-order local autocorrelation
calculating unit 26 by, for example, a principal component
analysis.
[0077] Then, in step S102, the result output unit 28 outputs the
characteristic value output from the dimensional compression unit
27. In this case, the result output unit 28 may output the result
of the dimensional compression process by the dimensional
compression unit 27 in the form of, for example, data, text, audio,
or an image.
[0078] Subsequently, in step S106, the recognizing device 3
recognizes the state of the detection area of the Doppler sensor 1
based on the characteristic value output from the result output
unit 28. Specifically, the recognizing device 3 recognizes whether
the state of the space is any one of unmanned, rest, and activity
states by applying a recognizer that is based on a support vector
machine or a hidden Marks model.
[0079] The operation of the state system according to this
embodiment has been described above. Next, advantages exhibited by
the state system according to this embodiment will be described
with reference to FIG. 4 to FIG. 6.
[2.3. Advantages]
[0080] By using higher-order local autocorrelation, the
characteristic-value calculating device 2 according to this
embodiment can extract a characteristic value, such as a change in
phase difference and the periodicity of movement (human body
movement or a biological signal) within a target area, different
from signal amplitude or frequency. Therefore, the recognizing
device 3 can distinguish a human from other disturbance even in a
case where signal intensity similar to a Doppler signal obtained
from human biological information is obtained, such as when there
is a small animal, or in a case where there is an electrical
household device that operates in a cycle similar to the human
respiration cycle. Thus, the recognizing device 3 can more
accurately detect the state within the detection area based on the
characteristic value calculated by the characteristic-value
calculating device 2.
[0081] The advantages exhibited by the state system according to
this embodiment will be described below in comparison with a
technique for extracting a characteristic value by fast Fourier
transform (FFT), which is one of techniques in the related art, as
a comparative example. In the comparative example, a Doppler signal
in a predetermined interval is converted into a frequency domain by
FFT, and an average frequency of the same frequency domain as the
characteristic-value calculating device 2 is calculated as a
characteristic value. FIG. 4 and FIG. 5 illustrate the distribution
of manned and unmanned states of average frequencies calculated in
the comparative example.
[0082] FIG. 4 is a diagram illustrating frequency distribution of
average frequencies by FFT in a one-second interval. FIG. 5 is a
diagram illustrating frequency distribution of average frequencies
by FFT in a ten-second interval. As shown in FIG. 4, with FFT in
the one-second interval, it is difficult to determine differences
in characteristic values based on manned and unmanned states. On
the other hand, as shown in FIG. 5, with FFT in the ten-second
interval, it is clear that the manned-state characteristic values
are distributed as larger values than the unmanned-state
characteristic values. In other words, the characteristic values
according to FFT in the ten-second interval are considered to be
useful for determining manned and unmanned states. However, it can
also be said that characteristic values according to FFT in the
examples shown in FIG. 4 and FIG. 5 become useful for determining
manned and unmanned states only after there is data from a long
interval of about ten seconds.
[0083] In contrast, the higher-order local autocorrelation
according to the embodiment of the present invention is useful for
determining manned and unmanned states even with data from a short
interval of about one second. FIG. 6 illustrates the distribution
of manned and unmanned states with respect to a first principal
component of higher-order local autocorrelation calculated by the
characteristic-value calculating device 2 relative to a Doppler
signal in a one-second interval when the correlation width t is set
to 0.5 seconds (250 samples if the sampling frequency is 500
Hz).
[0084] FIG. 6 is a diagram illustrating frequency distribution of
the first principal component of higher-order local autocorrelation
in a one-second interval in accordance with the embodiment of the
present invention. As shown in FIG. 6, it is clear that the
manned-state characteristic values are distributed as larger values
than the unmanned-state characteristic values even in the
one-second interval. Accordingly, the characteristic-value
calculating device 2 according to the embodiment of the present
invention can calculate a characteristic value useful for
determining manned and unmanned states even with data from a short
interval, with which it is difficult to calculate a useful
characteristic value in the comparative example.
[0085] Furthermore, for example, in a case where the sampling
frequency is set to 500 Hz, the number of samples subjected to
calculation by FFT in the one-second interval is 500 in the
comparative example. In contrast, in the characteristic-value
calculating device 2 according to the embodiment of the present
invention, six points, namely, V.sub.I(t), V.sub.I(t+.tau.),
V.sub.I(t+2.tau.), V.sub.Q(t), V.sub.Q(t+.tau.), and
V.sub.Q(t+2.tau.), are subjected to calculation even in the
one-second interval.
[0086] In other words, the characteristic-value calculating device
2 according to the embodiment of the present invention can
significantly reduce the number of samples subjected to
calculation, as compared with the comparative example, and can
consequently reduce the calculation amount for calculating a
characteristic value.
[0087] The advantages exhibited by the state system according to
this embodiment have been described above.
<3. Conclusion>As described above, the characteristic-value
calculating device 2 according to this embodiment can extract a
characteristic value for more accurately recognizing the state of a
moving object, such as a human, from a Doppler signal. Moreover,
the characteristic-value calculating device 2 can calculate a
useful characteristic value with short-interval data, as compared
with the technique in the related art. Furthermore, the
characteristic-value calculating device 2 can reduce the
calculation amount for calculating a characteristic value, as
compared with the technique in the related art.
[0088] Heretofore, preferred embodiments of the present invention
have been described in detail with reference to the appended
drawings, but the present invention is not limited thereto. It
should be understood by those skilled in the art that various
changes and alterations may be made without departing from the
spirit and scope of the appended claims.
[0089] For example, although the state of the detection area with a
human as a target object is recognized in the above embodiment, the
present invention is not limited to this example. For example, the
characteristic-value calculating device 2 may set a component to be
filtered by the filtering unit 23 as a frequency band arising from
biological information, such as respiration or heartbeat of an
animal other than humans, so as to set an arbitrary moving object
other than humans as a target object.
[0090] Furthermore, a computer program for causing hardware units,
such as a CPU, a ROM, and a RAM, contained in an information
processing device to exhibit functions similar to the components in
the above-described state recognizing system can also be created.
Moreover, a recording medium having such a computer program stored
therein is also provided.
REFERENCE SIGNS LIST
[0091] 1 Doppler sensor [0092] 2 characteristic-value calculating
device [0093] 21 preprocessing unit [0094] 22 data storage unit
[0095] 23 filtering unit [0096] 24 subsampling unit [0097] 25
vector normalization unit [0098] 26 higher-order local
autocorrelation calculating unit [0099] 27 dimensional compression
unit [0100] 28 result output unit [0101] 3 recognizing device
* * * * *